# microcosm-sqlite
Opinionated data loading with SQLite.
While most distributed application runtimes will use a networked data store for mutable state,
the usage patterns of data that is read-only at runtime are great fit for SQLite.
In particular, `microcosm-sqlite` assumes that applications will
- Build data sets in advance and ship them as static artifacts (e.g. in source control)
- Load data immutable sets at runtime without loading entire data sets into memory
## Writing Models
Persistent data is expected to use SQLAlchemy's declarative base classes. Because different data sets
may be shipped in different SQLite databases, each declarative base class needs to have a **unique**
name and a separate engine configuration, which is achieved by adding `DataSet` as the base of the
declarative base class:
Base = DataSet.create("some_name")
class SomeModel(Base):
__tablename__ = "sometable"
id = Column(Integer, primary_key=True)
## Using Stores
Basic persistence operations are abstracted through a store:
class SomeStore(Store):
@property
def model_class(self):
return SomeModel
store = SomeStore()
results = store.search()
## Configuring SQLite
Each `DataSet` defaults to using `:memory:` storage, but can be customized in two ways:
1. The `SQLiteBindFactory` can be configured with custom paths:
loader = load_from_dict(
sqlite=dict(
paths={
"some_name": "/path/to/database",
},
),
)
graph = create_object_graph("example", loader=loader)
2. The `microcosm.sqlite` entrypoint can contain a mapping from a data set name to a
function that returns a path.
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"description": "# microcosm-sqlite\n\nOpinionated data loading with SQLite.\n\nWhile most distributed application runtimes will use a networked data store for mutable state,\nthe usage patterns of data that is read-only at runtime are great fit for SQLite.\n\nIn particular, `microcosm-sqlite` assumes that applications will\n\n - Build data sets in advance and ship them as static artifacts (e.g. in source control)\n - Load data immutable sets at runtime without loading entire data sets into memory\n\n\n## Writing Models\n\nPersistent data is expected to use SQLAlchemy's declarative base classes. Because different data sets\nmay be shipped in different SQLite databases, each declarative base class needs to have a **unique**\nname and a separate engine configuration, which is achieved by adding `DataSet` as the base of the\ndeclarative base class:\n\n Base = DataSet.create(\"some_name\")\n\n\n class SomeModel(Base):\n __tablename__ = \"sometable\"\n\n id = Column(Integer, primary_key=True)\n\n\n## Using Stores\n\nBasic persistence operations are abstracted through a store:\n\n class SomeStore(Store):\n\n @property\n def model_class(self):\n return SomeModel\n\n\n store = SomeStore()\n results = store.search()\n\n\n## Configuring SQLite\n\nEach `DataSet` defaults to using `:memory:` storage, but can be customized in two ways:\n\n 1. The `SQLiteBindFactory` can be configured with custom paths:\n\n loader = load_from_dict(\n sqlite=dict(\n paths={\n \"some_name\": \"/path/to/database\",\n },\n ),\n )\n graph = create_object_graph(\"example\", loader=loader)\n\n 2. The `microcosm.sqlite` entrypoint can contain a mapping from a data set name to a\n function that returns a path.\n",
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